Nonprofits in a Time of Turbulence: Challenges and Opportunities
Bibliographic record
Abstract
Abstract We have entered a period of turbulent economic and political change. Internationally, slower growth coupled with youth unemployment and rising inequality have driven a renewed interest in social policy. In the US, the preferred policy approach since the 1990s has been to move away from cash assistance to direct service provision spurring demand for nonprofit services at the local level (Smith 2015, “Managing Human Service Organizations in the 21st Century.” Human Service Organizations: Management, Leadership, & Governance 39 (5):407–411). Recently, however, we have observed a power backlash against trade, immigration and economic insecurity that is reshaping politics and bringing about significant cuts in social service programs and health care at a time when the need is high. Fiscal scarcity will no doubt create an additional burden for nonprofits working with communities in need. In Canada, the federal government is moving in the opposite direction with greater investment in the social policy fields, including healthcare, childcare, housing and poverty reduction initiative. These investments will mean a greater flow of resources to the nonprofit sector, but the government has been clear that in exchange they want to tie funding to results and performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".